Bieler Jonathan, Pozzorini Christian, Garcia Jessica, Tuck Alex C, Macheret Morgane, Willig Adrian, Couraud Sébastien, Xing Xiaobin, Menu Philippe, Steinmetz Lars M, Payen Léa, Xu Zhenyu
SOPHiA GENETICS SA, Saint Sulpice, Switzerland.
Laboratoire de Biochimie et Biologie Moléculaire, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, Pierre Bénite, France.
JCO Clin Cancer Inform. 2021 Oct;5:1085-1095. doi: 10.1200/CCI.21.00057.
The ability of next-generation sequencing (NGS) assays to interrogate thousands of genomic loci has revolutionized genetic testing. However, translation to the clinic is impeded by false-negative results that pose a risk to patients. In response, regulatory bodies are calling for reliability measures to be reported alongside NGS results. Existing methods to estimate reliability do not account for sample- and position-specific variability, which can be significant. Here, we report an approach that computes reliability metrics for every genomic position and sample interrogated by an NGS assay.
Our approach predicts the limit of detection (LOD), the lowest reliably detectable variant fraction, by taking technical factors into account. We initially explored how LOD is affected by input material amount, library conversion rate, sequencing coverage, and sequencing error rate. This revealed that LOD depends heavily on genomic context and sample properties. Using these insights, we developed a computational approach to predict LOD on the basis of a biophysical model of the NGS workflow. We focused on targeted assays for cell-free DNA, but, in principle, this approach applies to any NGS assay.
We validated our approach by showing that it accurately predicts LOD and distinguishes reliable from unreliable results when screening 580 lung cancer samples for actionable mutations. Compared with a standard variant calling workflow, our approach avoided most false negatives and improved interassay concordance from 94% to 99%.
Our approach, which we name LAVA (LOD-aware variant analysis), reports the LOD for every position and sample interrogated by an NGS assay. This enables reliable results to be identified and improves the transparency and safety of genetic tests.
新一代测序（NGS）检测能够对数千个基因组位点进行检测，这彻底改变了基因检测技术。然而，向临床应用的转化受到假阴性结果的阻碍，这些结果对患者构成风险。作为回应，监管机构要求在报告NGS结果的同时报告可靠性指标。现有的估计可靠性的方法没有考虑到样本和位置特异性的变异性，而这种变异性可能很大。在此，我们报告一种方法，该方法可为NGS检测所检测的每个基因组位置和样本计算可靠性指标。
我们的方法通过考虑技术因素来预测检测限（LOD），即最低可可靠检测到的变异分数。我们首先探讨了输入材料量、文库转化率、测序覆盖度和测序错误率如何影响LOD。这表明LOD在很大程度上取决于基因组背景和样本特性。利用这些见解，我们基于NGS工作流程的生物物理模型开发了一种计算方法来预测LOD。我们专注于游离DNA靶向检测，但原则上该方法适用于任何NGS检测。
我们通过对580个肺癌样本进行可操作突变筛查时，验证了我们的方法能够准确预测LOD，并区分可靠结果与不可靠结果。与标准变异检出工作流程相比，我们的方法避免了大多数假阴性，并将检测间一致性从94%提高到99%。
我们将该方法命名为LAVA（LOD感知变异分析），它报告了NGS检测所检测的每个位置和样本的LOD。这使得能够识别可靠结果，并提高了基因检测的透明度和安全性。
